Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback

Albeit, the implicit feedback based recommendation problem - when only the user history is available but there are no ratings - is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-of-the-art algorithms that are efficient on the explicit case cannot be straightforwardly transformed to the implicit case if scalability should be maintained. There are few if any implicit feedback benchmark datasets, therefore new ideas are usually experimented on explicit benchmarks. In this paper, we propose a generic context-aware implicit feedback recommender algorithm, coined iTALS. iTALS apply a fast, ALS-based tensor factorization learning method that scales linearly with the number of non-zero elements in the tensor. The method also allows us to incorporate diverse context information into the model while maintaining its computational efficiency. In particular, we present two such context-aware implementation variants of iTALS. The first incorporates seasonality and enables to distinguish user behavior in different time intervals. The other views the user history as sequential information and has the ability to recognize usage pattern typical to certain group of items, e.g. to automatically tell apart product types or categories that are typically purchased repetitively (collectibles, grocery goods) or once (household appliances). Experiments performed on three implicit datasets (two proprietary ones and an implicit variant of the Netflix dataset) show that by integrating context-aware information with our factorization framework into the state-of-the-art implicit recommender algorithm the recommendation quality improves significantly.Comment: Accepted for ECML/PKDD 2012, presented on 25th September 2012, Bristol, U

Wear rates in urban rail systems

A significant part of maintenance costs in urban rail systems (metro, tram, light rapid transit/light metro) is due to wheel-rail wear. Wear rates - measured for example as depth of wear per kilometre run (rolling stock) or per train passage (rails) - depend in a complex manner on several influence factors. Among the most important are key design factors of the rolling stock (wheel profiles, suspension characteristics), of the track (distribution of curve radii, characteristics of switches and crossings, rail profiles), of the wheel-rail interface (lubrication, materials in contact, ambient characteristics), and of operations (frequency of traction and braking, trainset inversion policy, maintenance policy etc.). When designing an urban rail system, all of these factors have to be under control in order to limit the costs due to wheel/rail reprofiling/grinding and replacement. The state of the art allows the calculation of wear rates given quantitative input regarding the above factors. However, it is difficult to find in the literature experimental values for calibration of wear models and indications on what is a reasonable state-of-the-art wear rate for any given type of urban rail system. In this paper we present a structured analysis of flange wear rates found in the literature and derived from the experience of the authors, for a variety of cases, including metros and mainline rail systems. We compare the wear rates and explain their relationship with the influence factors. We then relate the wear rates with the needs in terms of wheel reprofiling/replacement. We estimate ranges for the calibration coefficients of wear models. We present the results in a way as to allow the designer of urban rail systems to derive values for target wear rates according to their specific conditions without the need for complex simulations

Impact of foregrounds on Cosmic Microwave Background maps

We discuss the possible impact of astrophysical foregrounds on three recent exciting results of Cosmic Microwave Background (CMB) experiments: the WMAP measurements of the temperature-polarization (TE) correlation power spectrum, the detection of CMB polarization fluctuations on degree scales by the DASI experiment, and the excess power on arcminute scales reported by the CBI and BIMA groups. A big contribution from the Galactic synchrotron emission to the TE power spectrum on large angular scales is indeed expected, in the lower frequency WMAP channels, based on current, albeit very uncertain, models; at higher frequencies the rapid decrease of the synchrotron signal may be, to some extent, compensated by polarized dust emission. Recent measurements of polarization properties of extragalactic radio sources at high radio frequency indicate that their contamination of the CMB polarization on degree scales at 30 GHz is substantially below the expected CMB E-mode amplitude. Adding the synchrotron contribution, we estimate that the overall foreground contamination of the signal detected by DASI may be significant but not dominant. The excess power on arc-min scales detected by the BIMA experiment may be due to galactic-scale Sunyaev-Zeldovich effects, if the proto-galactic gas is heated to its virial temperature and its cooling time is comparable to the Hubble time at the epoch of galaxy formation. A substantial contamination by radio sources of the signal reported by the CBI group on scales somewhat larger than BIMA's cannot be easily ruled out.Comment: 10 pages, 5 figures, to appear in proc. int. conf. "Thinking, Observing and Mining the Universe", Sorrento, Sept. 200

Helioseismology and the solar age

The problem of measuring the solar age by means of helioseismology hasbeen recently revisited by Guenther & Demarque (1997) and by Weiss & Schlattl (1998). Different best values for $t_{\rm seis}$ and different assessment of the uncertainty resulted from these two works. We show that depending on the way seismic data are used, one may obtain the value $t_{\rm seis}\approx 4.6$ Gy, close to the age of the oldest meteorites, $t_{\rm met}=4.57$ Gy, like in the first paper, or above 5 Gy like in the second paper. The discrepancy in the seismic estimates of the solar age may be eliminated by assuming higher than the standard metal abundance and/or an upward revision of the opacities in the solar radiative interior.We argue that the most accurate and robust seismic measure of the solar age are the small frequency separations, $D_{\ell,n}=\nu_{l,n}-\nu_{\ell+1,n-1}$, for spherical harmonic degrees $\ell=0,2$ and radial orders $n\gg\ell$.The seismic age inferred by minimization of the sum of squared differences between the model and the solar small separations is $t_{\rm seis}=4.66\pm0.11$, a number consistent with meteoritic data.Our analysis supports earlier suggestions of using small frequency separations as stellar age indicators.Comment: 8 pages + 4 ps figures included, LaTeX file with l-aa.sty, submitted to Astronomy and Astrophysic

Collaborative Filtering via Group-Structured Dictionary Learning

Structured sparse coding and the related structured dictionary learning problems are novel research areas in machine learning. In this paper we present a new application of structured dictionary learning for collaborative filtering based recommender systems. Our extensive numerical experiments demonstrate that the presented technique outperforms its state-of-the-art competitors and has several advantages over approaches that do not put structured constraints on the dictionary elements.Comment: A compressed version of the paper has been accepted for publication at the 10th International Conference on Latent Variable Analysis and Source Separation (LVA/ICA 2012

Addressing Item-Cold Start Problem in Recommendation Systems using Model Based Approach and Deep Learning

Traditional recommendation systems rely on past usage data in order to generate new recommendations. Those approaches fail to generate sensible recommendations for new users and items into the system due to missing information about their past interactions. In this paper, we propose a solution for successfully addressing item-cold start problem which uses model-based approach and recent advances in deep learning. In particular, we use latent factor model for recommendation, and predict the latent factors from item's descriptions using convolutional neural network when they cannot be obtained from usage data. Latent factors obtained by applying matrix factorization to the available usage data are used as ground truth to train the convolutional neural network. To create latent factor representations for the new items, the convolutional neural network uses their textual description. The results from the experiments reveal that the proposed approach significantly outperforms several baseline estimators

Strangeness Production in pp,pA,AA Interactions at SPS Energies.HIJING Approach

In this report we have made a systematic study of strangeness production in proton-proton(pp),proton-nucleus(pA) and nucleus- nucleus(AA) collisions at CERN Super Proton Synchroton energies, using$\,\,\, HIJING\,\,\, MONTE \,\,\,CARLO \,\,\,MODEL$ \\ (version $HIJ.01$). Numerical results for mean multiplicities of neutral strange particles ,as well as their ratios to negatives hadrons($$) for p-p,nucleon-nucleon(N-N),\,\,p-S,\,\,p-Ag,\,\,p-Au('min. bias')collisions and p-Au,\,\,S-S,\,\,S-Ag,\,\,S-Au ('central')collisions are compared to experimental data available from CERN experiments and also with recent theoretical estimations given by others models. Neutral strange particle abundances are quite well described for p-p,N-N and p-A interactions ,but are underpredicted by a factor of two in A-A interactions for $\Lambda,\bar{\Lambda}, K^{0}_{S}$ in symmetric collisions(S-S,\,\,Pb-Pb)and for $\Lambda,\bar{\Lambda}\,\,$in asymmetric ones(S-Ag,\,\,S-Au,\,\,S-W). A qualitative prediction for rapidity, transverse kinetic energy and transverse momenta normalized distributions are performed at 200 GeV/Nucleon in p-S,S-S,S-Ag and S-Au collisions in comparison with recent experimental data. HIJING model predictions for coming experiments at CERN for S-Au, S-W and Pb-Pb interactions are given. The theoretical calculations are estimated in a full phase space.Comment: 33 pages(LATEX),18 figures not included,available in hard copy upon request , Dipartamento di Fisica Padova,report DFPD-94-NP-4

"Jnking” atherosclerosis

Abstract.: Numerous studies in animal models established a key role of the C-jun N-terminal kinase (JNK) family (JNK1, JNK2 and JNK3) in numerous pathological conditions, including cancer, cardiac hypertrophy and failure, neurodegenerative disorders, diabetes, arthritis and asthma. A possible function of JNK in atherosclerosis remained uncertain since conclusions have mainly been based on in vitro studies investigating endothelial cell activation, T-effector cell differentiation and proliferation of vascular smooth muscle cells, all of which represent crucial cellular processes involved in atherosclerosis. However, recent experiments demonstrated that macrophage-restricted deletion of JNK2 was sufficient to efficiently reduce atherosclerosis in mice. Furthermore, it has been shown that JNK2 specifically promotes scavenger receptor A-mediated foam cell formation, an essential step during early atherogenesis, which occurs when vascular macrophages internalize modified lipoproteins. Thus, specific inhibition of JNK2 activity may emerge as a novel and promising therapeutic approach to attenuate atheroma formation in the future. In this review, we discuss JNK-dependent cellular and molecular mechanisms underlying atherosclerosi

On Fermionic T-duality of Sigma modes on AdS backgrounds

We study the fermionic T-duality symmetry of integrable Green-Schwarz sigma models on AdS backgrounds. We show that the sigma model on $AdS_5\times S^1$ background is self-dual under fermionic T-duality. We also construct new integrable sigma models on $AdS_2\times CP^n$. These backgrounds could be realized as supercosets of SU supergroups for arbitrary $n$, but could also be realized as supercosets of OSp supergroups for $n=1,3$. We find that the supercosets based on SU supergroups are self-dual under fermionic T-duality, while the supercosets based on OSp supergroups are not. However, the reasons of OSp supercosets being not self-dual under fermionic T-duality are different. For $OSp(6|2)$ case, corresponding to $AdS_2\times CP^3$ background, the failure is due to the singular fermionic quadratic terms, just like $AdS_4\times CP^3$ case. For $OSp(3|2)$ case, the failure is due to the shortage of right number of $\kappa$-symmetry to gauge away the fermionic degrees of freedom, even though the fermionic quadratic term is not singular any more. More general, for the supercosets of the OSp supergroups with superalgebra $B(n,m)$, including $AdS_2\times S^{2n}$ and $AdS_4\times S^{2n}$ backgrounds, the sigma models are not self-dual under fermionic T-duality as well, obstructed by the $\kappa$-symmetry.Comment: 17 pages; Clarfications on kappa symmetries, references added;Published versio